The Time Efficiency Gain in Sharing and Reuse of Research Data

Abstract

Among the frequently stated benefits of sharing research data are time efficiency or increased productivity. The assumption is that reuse or secondary use of research data saves researchers time in not having to produce data for a publication themselves. This can make science more efficient and productive. However, if there is no reuse, time costs in making data available for reuse will have been made with no return on this investment. In this paper a mathematical model is used to calculate the break-even point for time spent sharing in a scientific community, versus time gain by reuse. This is done for several scenarios; from simple to complex datasets to share and reuse, and at different sharing rates. The results indicate that sharing research data can indeed cause an efficiency revenue for the scientific community. However, this is not a given in all modeled scenarios. The scientific community with the lowest reuse needed to reach a break-even point is one that has few sharing researchers and low time investments for sharing and reuse. This suggests it would be beneficial to have a critical selection of datasets that are worth the effort to prepare for reuse in other scientific studies. In addition, stimulating reuse of datasets in itself would be beneficial to increase efficiency in scientific communities.

The Open Innovation in Science research field: a collaborative conceptualisation approach

“…Costs for preparing research data to be reused are high, limiting sharing behaviour even among advocates (Fecher et al 2017;Plantin 2019). These costs, including time to format, annotate, and curate the data, as well as concerns over privacy, 'scooping', and misuse, must be balanced against the promised efficiencies of data reuse (Pronk 2019). One way to reduce these costs is by (real-time) storing and sharing of certain kinds of data automatically, without the need for human intervention (Rouder 2016).…”

PsyCuraDat: Designing a User-Oriented Curation Standard for Behavioral Psychological Research Data

“…Taking the perspective of a data provider, there are multi-faceted reasons for not sharing data ranging from legal concerns (e.g., the violation of participants’ privacy rights), a personal sense of ownership, the fear of “scooping” ( Longo and Drazen, 2016 ) and reputation loss ( Martone et al, 2018 ), all the way to the more infrastructural reasons like a lack of incentives ( Wallis et al, 2013 ; McKiernan et al, 2016 ) or discipline-specific research data centers. One of the most prominent reasons for not using data curation standards is the generally perceived extra effort associated with data management ( Blask and Förster, 2018 ; Pronk, 2019 ). This perception may be held by researchers because they are unfamiliar with the corresponding standards or because they do not feel well-equipped to use them ( Tenopir et al, 2015 ).…”

Research Data Recycling through Open Sharing and Reuse: A Case Study of Sustainable Digital Good Consumption in the Sharing Economy

“…Motivations for research data sharing and reusing are diverse and reflect the interests of many stakeholders including researchers, and funders (Stall et al, 2019;Wu et al, 2019;Chapman et al, 2020). Underlying the arguments for open data sharing include time efficiency gains (Pronk, 2019), providing others the ability to reproduce and verify past research (Allen & Mehler, 2019;Gray & Marwick, 2019;Hardwicke et al, 2019), allowing others to ask new questions using the data (Whitlock, 2011;Boté & Térmens, 2019;Pronk, 2019), advancing research and innovation (Borgman et al, 2019;Elsayed & Saleh, 2018), boosting faculty impact in their field of specialization through higher citations (Drachen et al, 2016;Colavizza et al, 2019;Park et al, 2018;Zeng et al, 2020), and finally making the outputs of government funded research available to the general public (Mayernik, 2017;Sholler et al, 2019;Zuiderwijk & Hinnant, 2019).…”